Interview: Paul Ross, SVP Marketing, Sentieo
How are you supporting clients during this challenging period?
As a corporate and financial research platform provider, we saw two big things happening at the start of the crisis. First, a huge thirst for information and data about what was happening in markets and with individual companies. We shifted a large percentage of our client facing resources to providing information about what our platform was showing was happening through Covid-19 dashboards, social media feeds, webinars, and blog posts. These kept clients informed and engaging with us. Secondly, we watched the shift to our clients' staff working from home closely. We knew that we had a cloud-based solution so had no concerns with users loosing access but that many of them would not be used to collaborating at a distance. We focused on helping them to use our research management system as a method to do that successfully, while maintaining confidence that nothing was being lost in hastily cobbled together consumer apps.
What do you think are the biggest challenges facing data scientists/AI experts/quantitative investors in 2020/2021? Why are they important?
The biggest challenge for AI investment for asset managers is going to be making the right decisions on where to buy or develop capabilities. There are a lot of expensive AI engineers and developers out there but we see too many firms opt to build what has already been developed by providers like us, even if it will take them a year and millions of dollars. The question, like any technology investment is where can my asset management firm get the best return and deliver something unique, instead of replicating things that not unique to my business.
What is going to be the biggest area of investment for Sentieo over the next 12 months?
Over the next 12 months, Sentieo’s investment is going to continue to focus on analyst and PM enablement. We’re building capabilities using NLP and ML that reduce low value task time and provide insights that you can only get from using these technologies with millions of sources of insights. Additionally, we are working with clients on how we can expose these capabilities to their developers and data scientists to build on top of our platform, saving them development time to deliver unique data science driven solutions.
There seems to be a lot of cynicism surrounding the use of alt data and its role in alpha generation and if you can truly find value from these datasets. How is Sentieo working around this/what are your views on the future of alternative data?
The challenge I see with alt data and investing is that it is treated as a secret weapon to be used in isolation and at the same time promoted as a panacea. As with any technology or data source it comes down to thoughtful application. Sentieo has embedded consumer behaviour datasets (search trends, social media engagement data) directly within the fundamental research workflow. We normalized them so they could be used alongside traditional financial datasets to show where things were diverging and thus where opportunities were to take a specific investment position – for example where analyst consensus on revenue for an online retailer diverge from what website traffic is saying about consumer engagement. We see pulling alternative data into the fundamental research workflow as the norm as we move forward.
Cloud computing has been widely adopted in most sectors except financial services. Is this now changing, and if so how will funds decide how and where to include external providers?
No doubt that shutting down offices across financial centers across the globe and shifting to 100% work from home in some cases accelerated the adoption of cloud computing technologies. Anything, that was dependent upon physical access or had financial impediments to remote access became a liability overnight. There remain some regulatory hurdles to broader cloud-based business systems but this is rapidly changing to an investment decision instead of a policy decision.
A portion of the industry are adamant that advanced ML techniques such as Reinforcement Learning and Deep Learning cannot be applied to financial data – do you agree?
What are the main challenges in preventing this from happening? The biggest challenges are clear use cases and application for ML techniques, not blanket policies related to their suitability. As with all technology this is a discussion related to applicability and workflow. A good example is the use of deep learning for categorization of statements in text documents. We use this approach for our Smart Summary earnings call analysis capabilities. There are too many applications of this technology to make such generalized statements.
What is your biggest professional achievement to date?
It is always has been and always will be building and growing great teams.
Don't miss out on hearing more form Paul as he joins Digital Week June 22 - 26.
Monday June 22: 10:50 am - 11:40 am Digital Debate: The reality of implementing AI within financial markets
Thursday June 25:10:15 am - 10:30 am Applying AI to Drive Alpha From Fundamental Research